The fabric dying technology decides a dye formula (e.g., proportions of a red dye, a yellow dye, and a blue dye) and a control data set of a plurality of control factors (e.g., starting temperature of the dominant dyeing process, temperature rise rate of the dominant dyeing process, endpoint temperature of the dominant dyeing process, the duration in which the dominant dyeing process maintains the maximum temperature, and bath ratio) for a fabric dyeing process in three stages. Briefly speaking, in the first stage, the user makes a test on a sample in the laboratory to preliminarily decide the dye formula and the control data set of a plurality of control factors for the fabric dyeing process. In the second stage, the dye formula and the control data set decided in the first state is applied in a dyeing vat of the factory. In the third stage, the dyeing result of the second stage is inspected (e.g., analyzing the consistency between the color of the finished product and the ordered color through use of a spectrometer). If the dyeing result fails to meet the expectation, the three stages have to be repeated until the dye formula and the control data set decided lead to the expected dyeing result. Since the dyeing production scale of the factory is much greater than that of the dyeing test in the laboratory (e.g., the cloth to be dyed in the dyeing vat of the factory may be tens of thousands or even hundreds of thousands of times of the sample used in the laboratory), repeating the aforementioned three stages will remarkably increase the cost of the fabric dyeing process.
Based on the above descriptions, whether the dye formula and the control data set decided in the laboratory in the first stage is able to meet the real requirement is critical. Currently, the dye formula for a fabric dyeing process can be decided by the well-established dye formula software (e.g., Datacolor MATCH) and, therefore, the dye formula does not have to be adjusted repeatedly. As to the control data set of control factors of the fabric dyeing process, it is decided by the manufacturer by experience. Deciding the control data set based on user's experience is inefficient. In addition, in case that a decided control data set is not good enough, the aforesaid three stages have to be repeated and a remarkable increase in the cost of the fabric dyeing process will be caused.
Accordingly, efforts have to be made in the art to efficiently and accurately decide a target control data set for a fabric dyeing process so as to meet the requirement of dyeing quality, increase the stability and reduce the production cost.